Methods and apparatus for adjusting model threshold levels

ABSTRACT

Methods and apparatus for adjusting model threshold levels are disclosed. An example apparatus includes memory; and processor circuitry to execute machine readable instructions to: generate an adjusted count of users by applying an adjustment factor based on volume estimates and an updated total volume target to an absolute count of users; and adjust the model threshold based on the adjusted count of users by: determining a decreased model threshold, the decreased model threshold being less than the model threshold; determining a decreased model total based on lookback model score data, the decreased model total corresponding to a number of users that studies the decreased model threshold; and when a first difference between (A) the decreased model total and (B) adjusted absolute count of users is smaller than a second difference, replacing the model threshold with the decreased model threshold.

RELATED APPLICATION

This patent arises from a continuation of U.S. patent application Ser.No. 16/688,986, entitled “METHODS AND APPARATUS FOR ADJUSTING MODELTHRESHOLD LEVELS,” filed on Nov. 19, 2019, which is a continuation ofU.S. patent application Ser. No. 15/377,678, entitled “METHODS ANDAPPARATUS FOR ADJUSTING MODEL THRESHOLD LEVELS,” filed on Dec. 13, 2016.Priority to U.S. patent application Ser. No. 16/688,986 and U.S. patentapplication Ser. No. 15/377,678 is claimed. U.S. patent application Ser.No. 16/688,986 and U.S. patent application Ser. No. 15/377,678 areincorporated herein by reference in their entireties.

FIELD OF THE DISCLOSURE

This disclosure relates generally to audience measurement, and, moreparticularly, to methods and apparatus for adjusting model thresholdlevels.

BACKGROUND

Traditionally, audience measurement entities have measured audienceengagement levels for media based on registered panel members. That is,an audience measurement entity (AME) enrolls people who consent to beingmonitored into a panel. The AME then monitors those panel members todetermine media (e.g., television programs, radio programs, movies,DVDs, advertisements, streaming media, websites, etc.) presented tothose panel members. In this manner, the AME can determine exposuremetrics for different media based on the collected media measurementdata.

Techniques for monitoring user access to Internet resources, such aswebpages, advertisements and/or other Internet-accessible media, haveevolved significantly over the years. Internet-accessible media is alsoknown as online media. Some known systems monitor online media primarilythrough server logs. In particular, entities serving media on theInternet can use known techniques to log the number of requests receivedat their servers for media.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an environment in which model thresholdscores are adjusted to achieve a total volume target and a modeledvolume target in a data segment.

FIG. 2 is a block diagram of an example model analyzer of FIG. 1.

FIGS. 3-4 are flowcharts representative of example machine readableinstructions that may be executed to implement the example modelanalyzer of FIGS. 1 and 2 to adjust model thresholds.

FIG. 5 is a block diagram of a processor platform structured to executethe example machine readable instructions of FIGS. 3 and 4 to controlthe example model analyzer of FIGS. 1 and 2.

The figures are not to scale. Wherever possible, the same referencenumbers will be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

Techniques for monitoring user access to Internet-accessible media, suchas websites, advertisements, content and/or other media, have evolvedsignificantly over the years. Internet-accessible media is also known asonline media. In the past, such monitoring was done primarily throughserver logs. In particular, entities serving media on the Internet wouldlog the number of requests received for their media at their servers.Basing Internet usage research on server logs is problematic for severalreasons. For example, server logs can be tampered with either directlyor via zombie programs, which repeatedly request media from the serverto increase the server log counts. Also, media is sometimes retrievedonce, cached locally and then repeatedly accessed from the local cachewithout involving the server. Server logs cannot track such repeat viewsof cached media. Thus, server logs are susceptible to both over-countingand under-counting errors.

The inventions disclosed in Blumenau, U.S. Pat. No. 6,108,637, which ishereby incorporated herein by reference in its entirety, fundamentallychanged the way Internet monitoring is performed and overcame thelimitations of the server-side log monitoring techniques describedabove. For example, Blumenau disclosed a technique wherein Internetmedia to be tracked is tagged with monitoring instructions. Inparticular, monitoring instructions (also known as a media impressionrequest) are associated with the hypertext markup language (HTML) of themedia to be tracked. When a client requests the media, both the mediaand the impression request are downloaded to the client. The impressionrequests are, thus, executed whenever the media is accessed, be it froma server or from a cache.

Impression requests cause monitoring data reflecting information aboutan access to the media to be sent from the client that downloaded themedia to a monitoring entity. Sending the monitoring data from theclient to the monitoring entity is known as an impression request.Typically, the monitoring entity is an AME that did not provide themedia to the client and who is a trusted (e.g., neutral) third party forproviding accurate usage statistics (e.g., The Nielsen Company, LLC).Advantageously, because the impression requests are associated with themedia and executed by the client browser whenever the media is accessed,the monitoring information is provided to the AME (e.g., via animpression request) irrespective of whether the client corresponds to apanelist of the AME.

There are many database proprietors operating on the Internet. Thesedatabase proprietors provide services to large numbers of subscribers.In exchange for the provision of services, the subscribers register withthe database proprietors. Examples of such database proprietors includesocial network sites (e.g., Facebook, Twitter, MySpace, etc.),multi-service sites (e.g., Yahoo!, Google, Axiom, Catalina, etc.),online retailer sites (e.g., Amazon.com, Buy.com, etc.), creditreporting sites (e.g., Experian), streaming media sites (e.g., YouTube,etc.), etc. These database proprietors set cookies and/or otherdevice/user identifiers on the client devices of their subscribers toenable the database proprietor to recognize their subscribers when theyvisit the database proprietor website.

The protocols of the Internet make cookies inaccessible outside of thedomain (e.g., Internet domain, domain name, etc.) on which they wereset. Thus, a cookie set in, for example, the amazon.com domain isaccessible to servers in the amazon.com domain, but not to serversoutside that domain. Therefore, although an AME might find itadvantageous to access the cookies set by the database proprietors, theyare unable to do so.

The inventions disclosed in Mainak et al., U.S. Pat. No. 8,370,489,which is incorporated by reference herein in its entirety, enable an AMEto leverage the existing databases of database proprietors to collectmore extensive Internet usage by extending the impression requestprocess to encompass partnered database proprietors and by using suchpartners as interim data collectors. The inventions disclosed in Mainaket al. accomplish this task by structuring the AME to respond toimpression requests from clients (who may not be a member of an audiencemember panel and, thus, may be unknown to the audience member entity) byredirecting the clients from the AME to a database proprietor, such as asocial network site partnered with the audience member entity, using animpression response. Such a redirection initiates a communicationsession between the client accessing the tagged media and the databaseproprietor. For example, the impression response received from the AMEmay cause the client to send a second impression request to the databaseproprietor. In response to receiving this impression request, thedatabase proprietor (e.g., Facebook) can access any cookie it has set onthe client to thereby identify the client based on the internal recordsof the database proprietor. In the event the client corresponds to asubscriber of the database proprietor, the database proprietorlogs/records a database proprietor demographic impression in associationwith the client/user and subsequently forwards logged databaseproprietor demographic impressions to the AME.

As used herein, an impression is defined to be an event in which a homeor individual accesses and/or is exposed to media (e.g., anadvertisement, content, a group of advertisements and/or a collection ofcontent). In Internet advertising, a quantity of impressions orimpression count is the total number of times media (e.g., content, anadvertisement or advertisement campaign) has been accessed by a webpopulation (e.g., the number of times the media is accessed). In someexamples, an impression or media impression is logged by an impressioncollection entity (e.g., an AME or a database proprietor) in response toa beacon request from a user/client device that requested the media. Insome examples, a media impression is not associated with demographics. Apanelist demographic impression is a media impression logged by an AMEfor which the AME has panelist demographics corresponding to a householdand/or audience member exposed to media. As used herein, a databaseproprietor demographic impression is an impression recorded by adatabase proprietor in association with corresponding demographicinformation provided by the database proprietor in response to a beaconrequest from a client device of a registered subscriber of the databaseproprietor.

In the event the client does not correspond to a subscriber of thedatabase proprietor, the database proprietor may redirect the client tothe AME and/or another database proprietor. If the client is redirectedto the AME, the AME may respond to the redirection from the firstdatabase proprietor by redirecting the client to a second, differentdatabase proprietor that is partnered with the AME. That second databaseproprietor may then attempt to identify the client as explained above.This process of redirecting the client from database proprietor todatabase proprietor can be performed any number of times until theclient is identified and the media exposure logged, or until alldatabase partners have been contacted without a successfulidentification of the client. In some examples, the redirections occurautomatically so the user of the client is not involved in the variouscommunication sessions and may not even know they are occurring.

Periodically or aperiodically, the partnered database proprietorsprovide their logs and demographic information to the AME, which thencompiles the collected data into statistical reports identifyingaudience members for the media.

Examples disclosed herein include generating models that score (e.g.,from zero to one) client device identifiers based on the media output onthe client device. In some examples, the client device identifiers(e.g., corresponding to client device users) are extracted from cookiesand/or data logs received at the audience measurement entity from one ormore database providers. Each model corresponds to a particularcharacteristic. For example, there may be a sports model thatcorresponds to sports, a luxury car model that corresponds to luxurycars, etc. The audience measurement entity generates modeled segments byscoring client device identifiers stored at the audience measuremententity according to how closely the media output by the client devicerelates to the particular model. The more the media output by the clientdevice corresponds to a characteristic of the model, the higher theaudience measurement entity will score the client device identifier. Forexample, if a user of a client device frequently visits websitesassociated with luxury items, the audience measurement entity will scorethe client device identifier with a high score (e.g., greater than 0.9)in a luxury watch model. In some examples, the audience measuremententity applies weights to client device identifiers to more closelyrepresent a universe of users. Additionally, the audience measuremententity generates data segments that include data related to the users(e.g., both modeled and deterministic) of client devices that satisfy amodel threshold. A model threshold is a particular score of a model. Forexample, if the model threshold is 0.75 for a model, the audiencemeasurement entity generates a data segment including users and/orclient device identifiers that received a score of 0.75 or more for themodel. As time progresses, the media output by the client devices and/orthe number of client device identifiers tracked by the audiencemeasurement entity may change. These changes create fluctuation in datasegments including fluctuating volume totals and fluctuating model scoredistributions. For example, a data segment generated for a firstduration of time may include 10 million users (e.g., client deviceusers) and the same data segment generated for a second duration of timemay include 30 million users. Examples disclosed herein includeadjusting the model threshold to substantially maintain a target number(e.g., volume) of users (e.g., both modeled and deterministic) in a datasegment. Adjusting a model threshold in an automated fashion is hereinreferred to as autothresholding. As used herein, deterministic users areusers that have been exposed to media corresponding to an audiencesegment. For examples, for the audience interest—golf, deterministicusers include users that have been exposed to golf media and/or golfproducts (e.g., via analysis of use of a client device, data from athird-party data collector (Experian, Facebook, etc.), credit cardrecords, GPS records, etc.). As used herein, modeled users are usersthat are likely to belong to an audience segment based on satisfying athreshold score (e.g., have likely been exposed to the media, but theexposure has not been verified). For example, for the golf model,modelled users may include users that have been exposed to sports mediaand/or media associated with golf (e.g., increasing their model score tosatisfy the threshold score).

Data segments may be sold to advertising companies to provide consumerdata to advertising companies. The value (e.g., demand) of such datasegments corresponds to the number of users included in the data segmentand the number of modeled users in the data segment (e.g., the largerthe segment, the higher the demand of the segment). Accordingly,examples disclosed herein process models to increase the total number ofusers and/or the modeled number of users in a data segment, therebyincreasing the revenue of such data segments. Examples disclosed hereinadjust the model threshold score (e.g., model threshold) so that aminimum total number of users is satisfied that creates a sufficientvolume to meet market demands and/or to keep total volume under control.Further, examples disclosed herein increases the number of modeled usersin order to achieve a minimum modeled threshold. Examples disclosedherein track historic models, estimate future modeled and deterministicestimates, and compute adjustment factors using the historic models andmodeled and deterministic estimates. Further, examples disclosed hereinapply the adjustment factors to model samples to generate desired countsof users and adjust model thresholds to satisfy the desired counts ofusers in a segment. In this manner, examples disclosed herein are ableto autothreshold in environments with fluctuating volume and model scoredistributions.

FIG. 1 illustrates an example environment including an example audiencemeasurement entity 100 to adjust model thresholds to achieve a desiredtotal volume of users in an example data segment 101. The example ofFIG. 1 includes example media 102 provided by an example content server103 to an example client device 104 via an example network 105. Theexample audience measurement entity (AME) 100 includes an example userdatabase 106, an example score determiner 108, an example model database110, an example model analyzer 112, and an example data segmentgenerator 114 to generate the example data segment 101.

The example content server 103 of FIG. 1 provides the example media 102to the example client device 104 via the example network 105. In someexamples, the content server 103 provides the media 102 in response to arequest from the example client device 104. The example media 102 mayinclude or otherwise correspond to instructions to be executed by theexample client device 104. The instructions may instruct the clientdevice 104 to send data (e.g., including media data, client deviceidentifiers, cookies, etc.) to the example AME 100, via the examplenetwork 105, when the example media 102 is accessed. In this manner, theexample AME 100 can identify when the media 102 has been accessed by theexample client device 104 and correspond the media 102 to the clientdevice 104. Although the example described in FIG. 1 includes theexample content server 103 to provide the example media 102 to theexample client device 104, thereby causing the example client device 104to transmit data to the example AME 100, there may be alternative waysof instructing the example client device 104 to transmit data to theexample AME 100 when the example media 102 is accessed. For example, theclient device 104 may have metering software running on the exampleclient device 104 to transmit data whenever any media, including but notlimited to the example media 102 of FIG. 1, is accessed. Alternatively,the example client device 104 may transmit the data to an additionalserver (e.g., a database proprietor server) and the additional servermay forward the data to the example AME 100.

The example client device 104 of FIG. 1 may be any device capable ofaccessing the example media 102 over the example network 105 (e.g., theInternet). For example, the client device 104 may be a mobile device, acomputer, a tablet, a smart television, and/or any otherInternet-capable device or appliance. Examples disclosed herein may beused to collect impression information for any type of the example media102 including content and/or advertisements. Media may includeadvertising and/or content delivered via websites, streaming video,streaming audio, Internet protocol television (IPTV), movies,television, radio and/or any other vehicle for delivering the examplemedia 102. In some examples, the example media 102 includesuser-generated media that is, for example, uploaded to media uploadsites, such as YouTube, and subsequently downloaded and/or streamed byone or more other client devices for playback. Media may also includeadvertisements. Advertisements are typically distributed with content(e.g., programming). Traditionally, content is provided at little or nocost to the audience because it is subsidized by advertisers that pay tohave their advertisements distributed with the content. As used herein,“media” refers collectively and/or individually to content and/oradvertisement(s).

The example client device 104 of FIG. 1 transmits device/useridentifiers including cookies, hardware identifiers, app storeidentifiers, open source unique device identifiers, and/or any othertype of identifier to the example AME 100 based on instructions (e.g.,beacon instructions) executed by the example client device 104. In someexamples, the example client device 104 transmits the identifiers to theexample AME 100 whenever the example client device 104 accesses taggedmedia (e.g., the example media 102 after being tagged by the example AME100). Alternatively, media exposure data from the example client device104 may be transmitted to the example AME 100 using any alternativemeans including, but not limited to, through a metering device, surveys,database proprietor data, etc. In this manner, the example AME 100 canmonitor and correlate user activity and/or media exposure to the exampleclient device 104. In some examples, the AME 100 may receive device/useridentifiers and/or exposure logs from a database proprietor that storesa cookie on the example client device 104.

The example network 105 of FIG. 1 is a communications network. Theexample network 105 allows the example media 102 from the examplecontent server 103 to be accessed by the example client device 104and/or allows the data from the example client device 104 to be accessedby the example the example AME 100. The example network 105 may be alocal area network, a wide area network, the Internet, a cloud, or anyother type of communications network.

The example AME 100 of FIG. 1 does not provide the media to the clientdevices 104 and is a trusted (e.g., neutral) third party (e.g., TheNielsen Company, LLC) for providing accurate media access (e.g.,exposure) statistics. The example user database 106 stores mediamonitoring information and/or demographic information corresponding to auser(s) of the example client device 104 based on the data sent by theexample client device 104 and/or a database proprietor to the exampleAME 100. Additionally, the example user database 106 stores mediamonitoring and/or demographic information corresponding to other clientdevices that send data. In some examples, the example user database 106includes multiple databases. For examples, the user database 106 mayinclude a modelled database and a deterministic database. The modelleddatabase may include modelled segments for identifiers of client devicesand/or users included in a particular model and media exposure datacorresponding to the media output by the client devices. Thedeterministic database may include deterministic segments foridentifiers for client devices that deterministically belong to aparticular model (e.g., based on media exposure, third party (e.g.,Experian, Facebook, etc.) data, credit data, etc.). In such examples, aclient device identifier may be included in the both the modelleddatabase and the deterministic database. For example, a user maycorrespond to a deterministic segment for a first model (e.g., hybridcars) and correspond to a modelled segment for a second model (e.g.,kitchen appliances).

The example score determiner 108 of FIG. 1 identifies a score for a userof the example client device 104, and all other users stored in theexample user database 106, based on (A) the example media 102 that wasaccessed by the example client device 104 and (B) the characteristics ofa model stored in the example model database 110. The score correspondsto how likely the user will relate to a particular advertisement and/ortype of advertisement. For example, if the model is a luxury watch modeldesigned to advertise to users in the market for a luxury watch, thescore determiner 108 may select a very high score (e.g., 0.98) to a userwho is exposed to lots of luxury items (e.g., cars, boats, houses, etc.)on the example client device 104. In such an example, the scoredeterminer 108 may select a high score (e.g., 0.80) for a user who isexposed to some luxury items. Additionally, the example score determiner108 may select a low score (e.g., 0.10) for a user who is not exposed toany non-luxury items on the example client device 104.

The example model database 110 of FIG. 1 stores data corresponding tovarious models. As described above, the models may correspond to a typeof customer. The models are used to generate and/or contribute theexample data segments 116 to enable advertisers to provide customizedadvertising to users based on their media exposure patterns. Examplemodels may include a college student model, a luxury car model, a sportsmodel, and/or any other type of model. In some examples, the modelsinclude a generated distribution of all the volume users identified foreach score stored in the example user database 106. In some examples,the distribution is a cumulative distribution. For example, thedistribution may illustrate that there are 30 million (e.g., all) usersthat have a 0.00 score, 29 million users that have a 0.10 or belowscore, . . . , 150,000 users that have a 0.90 or below score. In suchexamples, the raw scores are stored and the distributions are aggregatedperiodically and/or aperiodically.

The example model analyzer 112 of FIG. 1 adjusts model thresholds,thereby adjusting the volume of users (e.g., modeled users) included inthe example data segment 101. As further described below in conjunctionwith FIG. 2, the example model analyzer 112 may adjust model thresholdsso that the example data segment 101 includes a sufficient volume tosatisfy model parameters for a particular segment set by a user and/ormanufacture. For example, the example model analyzer 112 may lower amodel threshold to increase the volume of (e.g., total number of usersin) the example data segment 101 and/or may increase the threshold todecrease the volume of the data segment 101 based on previous and/orprojected model data. Once the example model analyzer 112 adjusts amodel threshold, the model analyzer 112 deploys a model with theadjusted threshold to the example data segment generator 114. Theexample data segment generator 114 generates the example data segment101 based on the deployed adjusted threshold model. As described above,the example data segment 101 includes data for users (e.g., bothdeterministic and modeled) that satisfy the adjusted threshold.

FIG. 2 is an example block diagram of the example model analyzer 112 ofFIG. 2, disclosed herein, to dynamically adjust model thresholds. Whilethe example model analyzer 112 is described in conjunction with theexample model analyzer 112 of FIG. 1, the model analyzer 112 may beutilized to adjust model thresholds from any model. The example modelanalyzer 112 includes an example interface(s) 200, an example volumeestimator 202, an example estimate adjuster 204, an example adjustmentfactor generator 206, and an example model threshold adjuster 208.

The example interface(s) 200 of FIG. 2 receives user model parameters,model data from the example model database 110, volume data from theexample user database 106, and current settings from the example modeldatabase 110. Additionally, the example user interface(s) 200 transmits(e.g., deploys) models with adjusted model thresholds to the exampledata segment generator 114.

The example volume estimator 202 of FIG. 2 estimates a modeled volume(e.g., total) based on model data from a lookback window. A lookbackwindow includes all data from a duration corresponding to the currentlyanalyzed model from a preset time to the current time. In some examples,the preset time corresponds to the last time the model threshold wasadjusted. Alternatively, the preset time of time may be a day from thecurrent time, a week from the current time, a month from the current,etc. In some examples, the volume estimator 202 projects the data fromthe duration to a second, longer, duration of time corresponding to theexample data segment 101. For example, the volume estimator 202 mayidentify 10 million deterministic and modeled users for a model based ona lookback window of one week and project the 10 million users to 43million users to generate a 30-day modeled volume used to generate theexample data segment 101 of FIG. 1.

The example estimate adjuster 204 of FIG. 2 adjusts the volume estimategenerated by the example volume estimator 202 to satisfy differentconditions. For example, the conditions may include a desired ratio of amodeled estimates total to a sum of a deterministic estimates total andthe modeled estimates total. The estimate adjuster 204 may determine thedeterministic estimate total from the volume estimate generated by theexample volume estimator 202. For example, the volume estimate for aparticular model may be 40 million users, including both deterministicestimates and modeled estimates. The example estimate adjuster 204 maydetermine that 39 million of the 40 million users are included in thedeterministic estimates and 1 million estimates are included in themodeled estimates. The example estimate adjuster 204 determines thatratio of the modeled estimates total to the volume estimate is 2.5%(e.g., 1/40). The example estimate adjuster 204 may adjust the modeledestimates to satisfy a minimum modeled threshold. For example, if theminimum modeled threshold is 25%, then the example estimate adjuster 204adjusts the modeled estimates to satisfy the 25% minimum

$\left( {{e.g.},{\frac{x}{x + y} = {{0.2}5}},} \right.$

where x is the model estimates total and y is the deterministicestimates total). Using the above example, the estimate adjuster 204adjusts the modeled estimates from 1 million to 13 million

$\left( {{e.g.},\ {\frac{x}{x + {39}} = {{0.2}5}},\ {x = 13}} \right).$

The example estimate adjuster 204 adjusts the modeled estimates byincreasing the number of modeled users. Additionally, the exampleestimate adjuster 204 may adjust the modeled estimates to increase thevolume estimates total to satisfy the target volume target. For example,if the volume estimates total is 40 million, the ratio of modeledestimates total to volume estimates total is above the minimum modeledthreshold, and the total volume target is 50 million, the exampleestimate adjuster 204 may increase the estimates total by an additional10 million modeled users to increase the volume estimates total to 50million. The example estimate adjuster 204 determines the sum of theadjusted model estimates total and the deterministic estimates total.The sum of the adjusted model estimates total and the deterministicestimates total is herein referred to as the adjusted volume target. Insome examples, such as when the minimum model threshold and the totalvolume target are already satisfied, the example estimate adjuster 204may not adjust the volume estimates from the example volume estimator202. In such examples, the adjusted volume target is the same as thetotal volume target.

The example adjustment factor generator 206 of FIG. 2 determines anadjustment factor based on the sum of the model estimates total and thedeterministic estimates total after being, or not being, adjusted by theexample estimate adjuster 204 (e.g., the adjusted volume target). Theexample adjustment factor generator 206 computes the adjustment factorby dividing the adjusted volume target generated by the example estimateadjuster 204 by the volume estimate generated by the example volumeestimator 202. For example, when the volume estimate is 40 million andthe adjusted volume target is 52 million, the example adjustment factorcomputes an adjustment factor of 1.3 (e.g., 52/40=1.3).

The example model threshold adjuster 208 of FIG. 2 adjusts the modelthreshold applied to a particular model to satisfy the total volumetarget for a sample using the adjustment factor generated by the exampleadjustment factor generator 206. The sample corresponds to an absolutecount of users that satisfies the currently utilized model threshold fora particular period of time. The currently utilized model threshold isobtained from the example model database 110 using an intelligent scanof the example model database 110. In some examples, the samplecorresponds to the absolute count of users that satisfies the currentmodel threshold for the duration of time that the current modelthreshold has been implemented. The example model threshold adjuster 208applies the adjustment factor calculated by the example adjustmentfactor generator 206 to the absolute count of users to determine anadjusted count of users that corresponds to the total volume target forthe example data segment 101 of FIG. 1. For example, if the absolutecount of users that satisfy a model threshold of 0.75 is 1 million for atwo-day sample and/or a percent sample (e.g., 5% random sample of alldata received during a two-day period) and the adjustment factor is 1.3,the example model threshold adjuster 208 determines that the adjustedcount of users is 1.3 million (e.g., 1 million*1.3).

Once the example model threshold adjuster 208 of FIG. 2 determines theadjusted count of users, the example model threshold adjuster 208analyzes model score data to adjust the model threshold to a modelthreshold that best corresponds to the adjusted count of users. In someexamples, such as when the model score data is a cumulative model (e.g.,each model score includes a number of users that has a score greaterthan or equal to the model score), the example model threshold adjuster208 identifies the model score whose cumulative number of users that isclosest to the adjusted count of users. If the model score data is notcumulative (e.g., each model score includes a number of users that hasthe model score), the example model threshold adjuster 208 adjusts themodel threshold one step at a time to determine when the total number ofusers that satisfies the model threshold score is closest to theadjusted count of users, as further described in conjunction with FIG.4.

While example manners of implementing the example model analyzer 112 ofFIG. 1 is illustrated in FIG. 2, elements, processes and/or devicesillustrated in FIG. 2 may be combined, divided, re-arranged, omitted,eliminated and/or implemented in any other way. Further, the exampleinterface(s) 200, the example volume estimator 202, the example estimateadjuster 204, the example adjustment factor generator 206, the examplemodel threshold adjuster 208, and/or, more generally, the example modelanalyzer 112 of FIG. 2, may be implemented by hardware, machine readableinstructions, software, firmware and/or any combination of hardware,machine readable instructions, software and/or firmware. Thus, forexample, any of the example interface(s) 200, the example volumeestimator 202, the example estimate adjuster 204, the example adjustmentfactor generator 206, the example model threshold adjuster 208, and/or,more generally, the example model analyzer 112 of FIG. 2 could beimplemented by analog and/or digital circuit(s), logic circuit(s),programmable processor(s), application specific integrated circuit(s)(ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example interface(s)200, the example volume estimator 202, the example estimate adjuster204, the example adjustment factor generator 206, the example modelthreshold adjuster 208, and/or, more generally, the example modelanalyzer 112 of FIG. 2 is/are hereby expressly defined to include atangible computer readable storage device or storage disk such as amemory, a digital versatile disk (DVD), a compact disk (CD), a Blu-raydisk, etc. storing the software and/or firmware. Further still, theexample model analyzer 112 of FIG. 2 includes elements, processes and/ordevices in addition to, or instead of, those illustrated in FIGS. 3 and4, and/or may include more than one of any or all of the illustratedelements, processes and devices.

A flowchart representative of example machine readable instructions forimplementing the example model analyzer 112 of FIG. 1 is shown in FIGS.3 and 4. In the examples, the machine readable instructions comprise aprogram for execution by a processor such as the processor 512 shown inthe example processor platform 500 discussed below in connection withFIG. 5. The program may be embodied in machine readable instructionsstored on a tangible computer readable storage medium such as a CD-ROM,a floppy disk, a hard drive, a digital versatile disk (DVD), a Blu-raydisk, or a memory associated with the processor 512, but the entireprogram and/or parts thereof could alternatively be executed by a deviceother than the processor 512 and/or embodied in firmware or dedicatedhardware. Further, although the example program is described withreference to the flowchart illustrated in FIGS. 3 and 4, many othermethods of implementing the example model analyzer 112 of FIGS. 1 and 2may alternatively be used. For example, the order of execution of theblocks may be changed, and/or some of the blocks described may bechanged, eliminated, or combined.

As mentioned above, the example processes of FIGS. 3 and 4 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a tangible computer readable storagemedium such as a hard disk drive, a flash memory, a read-only memory(ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIGS. 3 and 4 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended.

FIG. 3 is an example flowchart 300 representative of example machinereadable instructions that may be executed by the example model analyzer112 of FIGS. 1 and 2 to adjust model thresholds corresponding to theexample data segment 101 of FIG. 1. Although the instructions of FIG. 3are described in conjunction with the example model analyzer 112 ofFIGS. 1 and 2, the example instructions may be utilized by any type ofmodel analyzer.

At block 301, the example interface(s) 200 receive a total volume targetfor the example data segment 101, previous model score data, andprevious volume data. The total volume target refers to a total numberof users, both deterministic and modeled, that should be included in theexample data segment 101 (e.g., the total number of users that satisfiesthe model threshold). In some examples the total volume targetcorresponds to a universe of users. The lookback model data includesdata related to how the example score determiner 108 scores the users ofthe example user database 106 with a lookback window (e.g., apredetermined amount of time). For example, lookback model data mayinclude the total number of users that received each available scorewithin a 5-day lookback window (e.g., from 5 days ago until now). Thelookback volume data identifies the volume of users that satisfied thecurrent and/or previous model threshold within the lookback window. Insome examples, the lookback volume data includes both modeled anddeterministic breakdowns of lookback window. For example, the lookbackvolume data may determine that based on a 5-day lookback window at amodel threshold of 0.75, 10.5 million users satisfied the modelthreshold and/or were included in deterministic segments correspondingto the model, where 7.2 million are deterministic users and 3.3 millionare modeled users.

At block 302, the example volume estimator 202 determines volumeestimates based on a lookback window by projecting the lookback volumedata over a period of time. In some examples, the period of time may bethe period of time corresponding to the data segment 101. For example,if the data segment 101 includes data from a 30-day period, the examplevolume estimator 202 determines volume estimates by projecting thevolume data of the lookback window to the 30-day period. In such anexample, if the lookback window is five days and the total volume of thelookback window is seven million users (e.g., 3 million deterministicand 4 million modeled), then the example volume estimator 202 multipliesthe seven million users for the five-day period by six to determine avolume estimate total of forty-two million users (e.g., 18 milliondeterministic and 22 million modeled). The volume estimates include amodeled estimates total (e.g., the estimated number of modeled usersprojected over the time period) and a deterministic estimates total(e.g., the estimated number of deterministic users projected over thetime period).

At block 304, the example estimate adjuster 204 determines a modeledestimates total so that the ratio of the modeled estimates total and thedeterministic estimates total satisfies a minimum modeled threshold. Forexample, if the deterministic estimates total is thirty-nine millionusers, the example estimate adjuster 204 determines that in order tomeet a minimum modeled threshold of 25%, for example, the modeledestimates total should be at least 13 million modeled users.

$\left( {{e.g.},\ {\frac{x}{x + {39}} = {\left. \frac{1}{4}\rightarrow x \right. = 13}}} \right).$

At block 306, the example estimate adjuster 204 determines if the sum ofthe modeled estimates total and the deterministic estimates totalsatisfies the total volume target. Using the above example, the estimateadjuster 204 determines that the sum of the modeled estimates total andthe deterministic estimates total is 52 million (e.g., 39 milliondeterministic and 13 million modeled). If the received total volumetarget is 50 million, for example, the example estimate adjuster 204determines that the sum of the modeled estimates total and deterministicestimates total (e.g., 52 million) satisfies (e.g., is greater than orequal to) the total volume target (e.g., 50 million). If, however, thereceived total volume target is 80 million, for example, the exampleestimate adjuster 204 determines that the sum of the modeled estimatestotal and the deterministic estimates total (e.g., 52 million) does notsatisfy (e.g., is less than) the total volume target (e.g., 80 million).

If the example estimate adjuster 204 determines that the sum of themodeled estimates total and the deterministic estimates total satisfiesthe total volume target (block 306: YES), the process continues to block310. If the example estimate adjuster 204 determines that the sum of themodeled estimates total and the deterministic estimates total does notsatisfy the total volume target (block 306: NO), the example estimateadjuster 204 increases the model estimates total to satisfy the totalvolume target (block 308). For example, if the model estimates total is13 million, the deterministic estimates total is 39 million (e.g., thesum of the modeled estimates total and the deterministic estimates totalbeing 52 million), and the total volume target is 80 million, theexample estimate adjuster 204 increases the model estimates total of 13million by 28 million (e.g., 80 million-52 million) to get a modeledestimates total of 41 million. In this manner, both the total volumetarget and the minimum modeled threshold are satisfied.

At block 310, the example estimate adjuster 204 updates the total volumetarget based on the sum of the modeled estimates total and thedeterministic estimates totals that satisfy the minimum model thresholdand/or the total volume target. For example, if the sum of the modeledestimates total and the deterministic estimates total is 52 million andthe total volume target is 50 million, the example estimate adjuster 204updates the total volume target to 52 million. In some examples, the sumof the modeled estimates total and the deterministic estimates totalthat satisfies the minimum model threshold and/or total volume targetwill be the same as the total volume target. In such examples, the totalvolume target may not be updated.

At block 312, the example adjustment factor generator 206 generates anadjustment factor by dividing the sum of the modeled estimates total andthe deterministic estimates total by the volume estimates determined bythe example volume estimator 202. For example, if the sum is 52 millionand the volume estimates is 42 million, the adjustment factor generator206 generates an adjustment factor of 1.24 (e.g., 52/42). At block 314,the example model threshold adjuster 208 identifies the absolute countof users based on the current model threshold. The absolute count ofusers corresponds to the deterministic data and modelled data from thelookback volume data (e.g., the total number of users that satisfied thecurrent model threshold and the deterministic total of the model withinthe lookback period).

At block 316, the example model threshold adjuster 208 applies theadjustment factor to the absolute count of users. For example, if theabsolute count of users during a lookback period is 3 million users andthe adjustment factor is 1.24, the example model threshold adjuster 208multiplies the adjustment factor by the absolute count of users togenerate an adjusted count of users of 3.72 million users. At block 318,the example model threshold adjuster 208 adjusts the current modelthreshold to achieve the adjusted count of users (e.g., 3.72 million) inthe data segment corresponding to the model. For example, if the currentmodel threshold is set to 0.75 and applying the 0.75 model threshold,the data segment will include 2.1 million users, the example modelthreshold adjuster 208 will decrease the model threshold to a lowerscore to increase the number of users to a number closer to 3.72 millionadjusted count of users, as further described below in conjunction withFIG. 4. At block 320, the example interface(s) 200 deploys the updatedmodel with the adjusted model thresholds. In some examples, theinterface(s) 200 deploys the updated model to by stored in the examplemodel database 110. In some examples, the interface(s) 200 deploys theupdated model to the example data segment generator 114 to generate theexample data segment 101.

FIG. 4 is an example flowchart 318 representative of example machinereadable instructions that may be executed to implement the examplemodel analyzer 112 of FIGS. 1 and 2 to adjust the current modelthreshold to achieve an adjusted count of users in the example datasegment 101, as described above in conjunction with block 318 of FIG. 3.Although the instructions of FIG. 3 are described in conjunction withthe example model analyzer 112 of FIGS. 1 and 2, the exampleinstructions may be utilized by any type of model analyzer.

At block 400, the example model threshold adjuster 208 determines if theadjusted count of users (e.g., after applying the adjustment factor) isgreater than the absolute count of users. The adjusted count of users isgreater than the absolute count of users when the adjustment factor isgreater than one. The adjusted count of users is less than the absolutecount of users when the adjustment factor is less than one. If theexample model threshold adjuster 208 determines that the adjusted countof users is greater than the absolute count of users (block 400: YES),the example model threshold adjuster 208 decreases the model thresholdto the next highest available model threshold (block 402). For example,if the current model threshold is 0.57 and the model thresholds are 0.01units apart, the example model threshold adjuster 208 decreases themodel threshold to 0.56.

At block 404, the example model threshold adjuster 208 determines andstores the total number of users that satisfy the decreased modelthreshold (e.g., the decreased threshold model total) based on theprevious model score data. For example, if the lookback model score dataincludes a total number of users that received a particular model score,the example model threshold adjuster 208 sums up the totals for eachmodel score from 0.56 to the maximum model score (e.g., 1.00). In suchan example, the example model threshold adjuster 208 may determine that3.1 million users satisfy the 0.56 model threshold. Additionally, theexample model threshold adjuster 208 stores the 3.1 million inassociation with the 0.56 model threshold.

At block 406, the example model threshold adjuster 208 determines if thedecreased threshold model total (e.g., 3.1 million) is greater than theadjusted absolute count (e.g., 3.72 million). If the example modelthreshold adjuster 208 determines that the decreased threshold total(e.g., 3.1 million) is not greater than the adjusted absolute count(e.g., 3.72 million) (block 406: NO), the process returns to block 402to continue to decrease the model threshold until the decreasedthreshold total is greater than the adjusted absolute count. If theexample model threshold adjuster 208 determines that the decreasedthreshold total is greater than the adjusted absolute count (block 406:YES), the example model threshold adjuster 208 determines if thedecreased model threshold total is closer to the absolute count of usersthan the previously stored model threshold total (block 408). Forexample, if the decreased threshold total for 0.50 is 3.8 million andthe decreased threshold total for 0.51 is 3.7 million, the example modelthreshold adjuster 208 would determine that the previously storeddecreased model threshold total (e.g., 3.7 million) corresponding to themodel threshold of 0.51 is closer to the adjusted absolute count ofusers (e.g., 3.72 million) than the current model threshold total (e.g.,3.8 million) corresponding to 0.50.

If the example model threshold adjuster 208 determines that thedecreased model threshold total is closer to the adjusted absolute countof users than the previously stored model threshold total (block 408:YES), the example model threshold adjuster 208 adjusts the current modelthreshold to the model threshold corresponding to the decreased modelthreshold total (block 410). If the example model threshold adjuster 208determines that the decreased model threshold total is not closer to theadjusted absolute count of users than the previously stored modelthreshold total (block 408: NO), the example model threshold adjuster208 adjusts the current model threshold to the model thresholdcorresponding to the previously stored decreased model threshold total(block 412). Alternatively, the example model threshold adjuster 208 mayselect a stored model threshold corresponding to a model threshold totalthat includes more than the adjusted absolute count, as opposed to amodel threshold corresponding to a model threshold total closest to theabsolute count.

Returning to block 400, if the example model threshold adjuster 208determines that the adjusted count of users is not greater than theabsolute count of users (block 400: NO), the example model thresholdadjuster 208 increases the model threshold to the next lowest availablemodel threshold (block 414). For example, if the current model thresholdis 0.57 and the model thresholds are 0.01 units apart, the example modelthreshold adjuster 208 increases the model threshold to 0.58.

At block 416, the example model threshold adjuster 208 determines andstores the total number of users that satisfy the increased modelthreshold (e.g., the increased threshold model total) based on theprevious model score data. For example, if the lookback model score dataincludes a total number of users that received a particular model score,the example model threshold adjuster 208 sums up the totals for eachmodel score from 0.58 to the maximum model score (e.g., 1.00). In suchan example, the example model threshold adjuster 208 may determine that4.2 million users satisfy the 0.58 model threshold. Additionally, theexample model threshold adjuster 208 stores the 4.2 million inassociation with the 0.58 model threshold.

At block 418, the example model threshold adjuster 208 determines if theincreased threshold model total (e.g., 4.2 million) is less than theadjusted absolute count (e.g., 3.72 million). If the example modelthreshold adjuster 208 determines that the increased threshold total(e.g., 4.2 million) is not less than the adjusted absolute count (e.g.,3.72 million) (block 418: NO), the process returns to block 414 tocontinue to increase the model threshold until the increased thresholdtotal is less than the adjusted absolute count. If the example modelthreshold adjuster 208 determines that the increase threshold total isless than the adjusted absolute count (block 418: YES), the examplemodel threshold adjuster 208 determines if the increased model thresholdtotal is closer to the absolute count of users than the previouslystored model threshold total (block 420). For example, if the increasedthreshold total for 0.60 is 3.6 million and the increased thresholdtotal for 0.59 is 3.8 million, the example model threshold adjuster 208would determine that the previously stored increased model thresholdtotal (e.g., 3.8 million) corresponding to the model threshold of 0.59is closer to the adjusted absolute count of users (e.g., 3.72 million)than the current model threshold total (e.g., 3.6 million) correspondingto 0.60.

If the example model threshold adjuster 208 determines that theincreased model threshold total is closer to the adjusted absolute countof users than the previously stored model threshold total (block 420:YES), the example model threshold adjuster 208 adjusts the current modelthreshold to the model threshold corresponding to the increased modelthreshold total (block 422). If the example model threshold adjuster 208determines that the increased model threshold total is not closer to theadjusted absolute count of users than the previously stored modelthreshold total (block 420: NO), the example model threshold adjuster208 adjusts the current model threshold to the model thresholdcorresponding to the previously stored increased model threshold total(block 424). Alternatively, the example model threshold adjuster 208 mayselect a stored model threshold corresponding to a model threshold totalthat includes less than the adjusted absolute count, as opposed to amodel threshold corresponding to a model threshold total closest to theabsolute count.

FIG. 5 is a block diagram of an example processor platform 500 capableof executing the instructions of FIG. 3 to implement the example modelanalyzer 112 of FIGS. 1 and 2. The processor platform 500 can be, forexample, a server, a personal computer, a mobile device (e.g., a cellphone, a smart phone, a tablet such as an iPad™), a personal digitalassistant (PDA), an Internet appliance, or any other type of computingdevice.

The processor platform 500 of the illustrated example includes aprocessor 512. The processor 512 of the illustrated example is hardware.For example, the processor 512 can be implemented by integratedcircuits, logic circuits, microprocessors or controllers from anydesired family or manufacturer.

The processor 512 of the illustrated example includes a local memory 513(e.g., a cache). The example processor 512 of FIG. 5 executes theinstructions of FIG. 3 to implement the example interface(s) 200, theexample volume estimator 202, the example estimate adjuster 204, theexample adjustment factor generator 206, and/or the example modelthreshold adjuster 208 of FIG. 2 to implement the example model analyzer112. The processor 512 of the illustrated example is in communicationwith a main memory including a volatile memory 514 and a non-volatilememory 516 via a bus 518. The volatile memory 514 may be implemented bySynchronous Dynamic Random Access Memory (SDRAM), Dynamic Random AccessMemory (DRAM), RAIVIBUS Dynamic Random Access Memory (RDRAM) and/or anyother type of random access memory device. The non-volatile memory 516may be implemented by flash memory and/or any other desired type ofmemory device. Access to the main memory 514, 516 is controlled by aclock controller.

The processor platform 500 of the illustrated example also includes aninterface circuit 520. The interface circuit 520 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 522 are connectedto the interface circuit 520. The input device(s) 522 permit(s) a userto enter data and commands into the processor 512. The input device(s)can be implemented by, for example, a sensor, a microphone, a camera(still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 524 are also connected to the interfacecircuit 520 of the illustrated example. The output devices 524 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, and/or speakers). The interface circuit 520 of theillustrated example, thus, typically includes a graphics driver card, agraphics driver chip or a graphics driver processor.

The interface circuit 520 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network526 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 500 of the illustrated example also includes oneor more mass storage devices 528 for storing software and/or data.Examples of such mass storage devices 528 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 532 of FIGS. 3 and 4 may be stored in the massstorage device 528, in the volatile memory 514, in the non-volatilememory 516, and/or on a removable tangible computer readable storagemedium such as a CD or DVD.

From the foregoing, it would be appreciated that the above disclosedmethod, apparatus, and articles of manufacture adjust model thresholdlevels in data segment generation. Examples disclosed herein increaserevenue by maintaining a minimum ratio of modeled users to total usersthat satisfy a model threshold. Additionally, examples disclosed hereinincrease revenue by increasing the number of modeled users to achieve atotal volume target that is representative of a universe of users. Usingexamples disclosed herein, models may be updated in substantiallyreal-time to meet market demands and increase revenue in a constantlyfluctuating environment.

Although certain example methods, apparatus and articles of manufacturehave been described herein, other implementations are possible. Thescope of coverage of this patent is not limited thereto. On thecontrary, this patent covers all methods, apparatus and articles ofmanufacture fairly falling within the scope of the claims of thispatent.

What is claimed is:
 1. A non-transitory computer readable storage mediumcomprising instructions which, when executed, cause one or moreprocessors to at least: generate an adjusted count of users by applyingan adjustment factor based on volume estimates and an updated totalvolume target to an absolute count of users, the volume estimatescorresponding to a number of modeled users that satisfy a modelthreshold within a first period of time; and determine a decreased modelthreshold, the decreased model threshold being less than the modelthreshold; determine a decreased model total based on lookback modelscore data, the decreased model total corresponding to a number of usersthat studies the decreased model threshold; and when a first differencebetween (A) the decreased model total and (B) adjusted absolute count ofusers is smaller than a second difference between (A) a model thresholdtotal corresponding to the model threshold and (B) the adjusted absolutecount of users, replace the model threshold with the decreased modelthreshold.
 2. The computer readable storage medium of claim 1, whereinthe instructions cause the one or more processors to, when a ratio of(A) the number of modeled users that satisfy the model threshold withinthe first period of time to (B) a sum of the number of modeled usersthat satisfy the model threshold and a number of deterministic clientdevice users does not satisfy a minimum ratio threshold, increase thenumber of modeled users to satisfy the model threshold.
 3. The computerreadable storage medium of claim 2, wherein the number of users is afirst number of users, the updated total volume target is a total volumetarget updated based on the increased number of modeled users, the totalvolume target is a second number of users that satisfy the modelthreshold to be included in a data segment, and the second number ofusers includes a number of client device users and the number of modeledusers.
 4. The computer readable storage medium of claim 2, wherein theupdated total volume target is the sum of the increased number ofmodeled users that satisfy the model threshold and client device usersthat satisfy the model threshold.
 5. The computer readable storagemedium of claim 1, wherein the volume estimates include a number ofclient device users and a number of modeled client device users.
 6. Thecomputer readable storage medium of claim 1, wherein the instructionscause the one or more processors to determine the volume estimates bydetermining a first volume estimate corresponding to a second number ofusers that satisfy the model threshold within a second period of timeand projecting the second number of users based on a difference betweenthe first period of time and the second period of time.
 7. The computerreadable storage medium of claim 1, wherein the instructions cause theone or more processors to generate the adjustment factor by dividing theupdated total volume target by the volume estimates.
 8. The computerreadable storage medium of claim 1, wherein the number of modeled usersis a first number of modeled users and the absolute count of users is anumber of client device users and a second number of modeled users thatsatisfy the model threshold within a second period of time.
 9. Thecomputer readable storage medium of claim 1, wherein the instructionscause the one or more processors to adjust the model threshold based onthe adjusted count of users by determining a model threshold value that,when applied to a model, changes the absolute count of users to a countnearest to the updated total volume target.
 10. The computer readablestorage medium of claim 1, wherein the instructions cause the one ormore processors to deploy a model corresponding the adjusted modelthreshold, the model corresponding to a data segment including adjustedcount of users.
 11. An apparatus comprising: memory to store machinereadable instructions; and processor circuitry to execute the machinereadable instructions to: generate an adjusted count of users byapplying an adjustment factor based on volume estimates and an updatedtotal volume target to an absolute count of users, the volume estimatescorresponding to a number of modeled users that satisfy a modelthreshold within a first period of time; and determining a decreasedmodel threshold, the decreased model threshold being less than the modelthreshold; determining a decreased model total based on lookback modelscore data, the decreased model total corresponding to a number of usersthat studies the decreased model threshold; and when a first differencebetween (A) the decreased model total and (B) adjusted absolute count ofusers is smaller than a second difference between (A) a model thresholdtotal corresponding to the model threshold and (B) the adjusted absolutecount of users, replacing the model threshold with the decreased modelthreshold.
 12. The apparatus of claim 11, wherein the processorcircuitry is to, when a ratio of (A) the number of modeled users thatsatisfy the model threshold within the first period of time to (B) a sumof the number of modeled users that satisfy the model threshold and anumber of deterministic client device users does not satisfy a minimumratio threshold, increase the number of modeled users to satisfy themodel threshold.
 13. The apparatus of claim 12, wherein the number ofusers is a first number of users, the updated total volume target is atotal volume target updated based on the increased number of modeledusers, the total volume target is a second number of users that satisfythe model threshold to be included in a data segment, and the secondnumber of users includes a number of client device users and the numberof modeled users.
 14. The apparatus of claim 12, wherein the updatedtotal volume target is the sum of the increased number of modeled usersthat satisfy the model threshold and client device users that satisfythe model threshold.
 15. The apparatus of claim 11, wherein the volumeestimates include a number of client device users and a number ofmodeled client device users.
 16. The apparatus of claim 11, furtherincluding a volume estimator is to determine the volume estimates bydetermining a first volume estimate corresponding to a second number ofusers that satisfy the model threshold within a second period of timeand projecting the second number of users based on a difference betweenthe first period of time and the second period of time.
 17. Theapparatus of claim 11, wherein the processor circuitry is to generatethe adjustment factor by dividing the updated total volume target by thevolume estimates.
 18. The apparatus of claim 11, wherein the number ofmodeled users is a first number of modeled users and the absolute countof users is a number of client device users and a second number ofmodeled users that satisfy the model threshold within a second period oftime.
 19. The apparatus of claim 11, wherein the processor circuitry isto adjust the model threshold based on the adjusted count of users bydetermining a model threshold value that, when applied to a model,changes the absolute count of users to a count nearest to the updatedtotal volume target.
 20. An apparatus comprising: an adjustment factorgenerator to generate an adjusted count of users by applying anadjustment factor based on volume estimates and an updated total volumetarget to an absolute count of users, the volume estimates correspondingto a number of modeled users that satisfy a model threshold within afirst period of time; and a model threshold adjuster to: determine adecreased model threshold, the decreased model threshold being less thanthe model threshold; determine a decreased model total based on lookbackmodel score data, the decreased model total corresponding to a number ofusers that studies the decreased model threshold; and when a firstdifference between (A) the decreased model total and (B) adjustedabsolute count of users is smaller than a second difference between (A)a model threshold total corresponding to the model threshold and (B) theadjusted absolute count of users, replace the model threshold with thedecreased model threshold.